Distribution Network Reconfiguration Optimization Using a New Algorithm Hyperbolic Tangent Particle Swarm Optimization (HT-PSO)

This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tange...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) Vol. 17; no. 15; p. 3798
Main Authors: Puma, David W., Molina, Y. P., Atoccsa, Brayan A., Luyo, J. E., Ñaupari, Zocimo
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2024
Subjects:
Algorithms
delta optimized value
Discovery and exploration
distribution network reconfiguration
Efficiency
Energy
Genetic algorithms
hyperbolic tangent
Load
Mathematical optimization
Methods
Optimization
Outer space
particle swarm optimization
rate of change
ISSN:1996-1073, 1996-1073
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tangent function, which effectively limits the rate of change values, offering a significant improvement over traditional sigmoid function-based methods. A key feature of this new approach is the integration of a tunable parameter, δ, into the HT-PSO, enhancing the curve’s adaptability. The careful optimization of δ ensures superior control over the rate of change across the entire operational range. This enhanced control mechanism substantially improves the efficiency of the search and convergence processes in DNR. Comparative simulations conducted on 33- and 94-bus systems show an improvement in convergence, demonstrating a more exhaustive exploration of the search space than existing methods documented in the literature based on PSO and variations where functions are proposed for the rate of change of values.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en17153798